Spatio-temporal electrical grid emission factors effects on calculated GHG emissions of buildings in mixed-grid environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study compares the calculated greenhouse gas (GHG) emissions of buildings using two different methodologies in mixed-grid environments. Simulations were conducted using virtual models of 25 buildings and actual meteorological data over 2016–2018. The “Annual Method” using yearly average emission factors and the “Hourly Method” using consumption-based hourly emission factors were used to calculate GHG emissions. The study found that the hourly method provided a more accurate representation of GHG emissions, especially during peak grid demand. Furthermore, the study recommends using a zonal approach to building codes in terms of electrical grids similar to climate zones in current codes and standards while also prioritizing building types with the largest potential for emissions reductions. A case study in Ontario, Canada found that electrification via heat pump always results in GHG savings independent of year, building model, and city if keeping the calculation method the same between fuel-switching models. Future research is needed to improve the accuracy of GHG emissions calculations and understand the relationship between electrical load and GHG emissions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it